Sándor Bulcsú, Nowak Michael, Koglin Tim, Martin Laura, Gros Claudius
Department of Physics, Babes-Bolyai University, Cluj-Napoca, Romania.
Institute for Theoretical Physics, Goethe University Frankfurt, Frankfurt am Main, Germany.
Front Neurorobot. 2018 Jul 11;12:40. doi: 10.3389/fnbot.2018.00040. eCollection 2018.
Self-organized robots may develop attracting states within the sensorimotor loop, that is within the phase space of neural activity, body and environmental variables. Fixpoints, limit cycles and chaotic attractors correspond in this setting to a non-moving robot, to directed, and to irregular locomotion respectively. Short higher-order control commands may hence be used to kick the system from one self-organized attractor robustly into the basin of attraction of a different attractor, a concept termed here as kick control. The individual sensorimotor states serve in this context as highly compliant motor primitives. We study different implementations of kick control for the case of simulated and real-world wheeled robots, for which the dynamics of the distinct wheels is generated independently by local feedback loops. The feedback loops are mediated by rate-encoding neurons disposing exclusively of propriosensoric inputs in terms of projections of the actual rotational angle of the wheel. The changes of the neural activity are then transmitted into a rotational motion by a simulated transmission rod akin to the transmission rods used for steam locomotives. We find that the self-organized attractor landscape may be morphed both by higher-level control signals, in the spirit of kick control, and by interacting with the environment. Bumping against a wall destroys the limit cycle corresponding to forward motion, with the consequence that the dynamical variables are then attracted in phase space by the limit cycle corresponding to backward moving. The robot, which does not dispose of any distance or contact sensors, hence reverses direction autonomously.
自组织机器人可能会在感觉运动环路内，即在神经活动、身体和环境变量的相空间内，发展出吸引状态。在这种情况下，不动点、极限环和混沌吸引子分别对应于静止的机器人、定向运动和不规则运动。因此，简短的高阶控制命令可用于将系统从一个自组织吸引子有力地踢入另一个吸引子的吸引域，这里将这一概念称为踢控。在这种情况下，各个感觉运动状态充当高度顺应性的运动基元。我们研究了模拟和现实世界中的轮式机器人的踢控不同实现方式，对于这些机器人，不同轮子的动力学由局部反馈回路独立生成。反馈回路由速率编码神经元介导，这些神经元仅根据轮子实际旋转角度的投影来处理本体感觉输入。然后，神经活动的变化通过类似于蒸汽机车所用传动杆的模拟传动杆转化为旋转运动。我们发现，自组织吸引子格局既可以按照踢控的思路通过高级控制信号来改变，也可以通过与环境相互作用来改变。撞到墙壁会破坏对应于向前运动的极限环，结果是动力学变量随后在相空间中被对应于向后运动的极限环吸引。因此，没有任何距离或接触传感器的机器人会自动反转方向。
